Cognitive models of optimal sequential search with recall.

Many everyday decisions require sequential search, according to which available choice options are observed one at a time, with each observation involving some cost to the decision maker. In these tasks, decision makers need to trade-off the chances of finding better options with the cost of search. Optimal strategies in such tasks involve threshold decision rules, which terminate the search as soon as an option exceeding a reward value is found. Threshold rules can be seen as special cases of well-known algorithmic decision processes, such as the satisficing heuristic. Prior work has found that decision makers do use threshold rules, however the stopping thresholds observed in data are typically smaller than the (expected value maximizing) optimal threshold. We put forward an array of cognitive models and use parametric model fits on participant-level search data to examine why decision makers adopt seemingly suboptimal thresholds. We find that people's behavior is consistent with optimal search if we allow participants to display risk aversion, psychological effort cost, and decision error. Thus, decision makers appear to be able to search in a resource-rational manner that maximizes stochastic risk averse utility. Our findings shed light on the psychological factors that guide sequential decision making, and show how threshold models can be used to describe both computational and algorithmic aspects of search behavior.

Rheumatologic Disorders and the Nervous System.

PURPOSE: This article describes the neurologic manifestations of systemic autoimmune diseases. RECENT FINDINGS: Systemic autoimmune diseases can be associated with a wide spectrum of neurologic comorbidities involving the central and peripheral nervous systems. Systemic lupus erythematosus (SLE) can be associated with a number of manifestations predominantly affecting the central nervous system (CNS), whereas peripheral neuropathy is less common. Sjögren syndrome can be associated with peripheral neuropathy in 10% of cases and CNS disease in 2% to 5% of cases. The risk of stroke is increased in SLE, rheumatoid arthritis, temporal arteritis, psoriatic arthritis, and ankylosing spondylitis. Systemic vasculitides present most commonly with mononeuritis multiplex but can also affect the CNS. Cognitive dysfunction is a common symptom among patients with systemic autoimmune diseases, most commonly seen in patients with SLE or Sjögren syndrome. SUMMARY: Neurologic manifestations of systemic autoimmune disease are important to recognize, as they may often be the presenting manifestation leading to diagnosis of the systemic disease or may be associated with increased morbidity, other complications, or mortality. Timely diagnosis and institution of appropriate treatment, often requiring multidisciplinary care, is essential to minimize morbidity and decrease the risk of permanent neurologic deficits.

Linear integration and lexicographic models of choice: A cue weight learning perspective.

Choosing between options characterized by multiple cues can be a daunting task. People may integrate all information at hand or just use lexicographic strategies that ignore most of it. Notably, integrative strategies require knowing exact cue weights, whereas lexicographic heuristics can operate by merely knowing the importance order of cues. Here we study how using integrative or lexicographic strategies interacts with learning about cues. In our choice-learning-estimation paradigm people first make choices, learning about cues from the experienced qualities of chosen options, and then estimate qualities of new options. We developed delta-elimination (DE), a new lexicographic strategy that generalizes previous heuristics to any type of environment, and compared it to the integrative weighted-additive (WADD) strategy. Our results show that participants learned cue weights, regardless of whether the DE strategy or the WADD strategy described their choices the best. Still, there was an interaction between the adopted strategy and the cue weight learning process: the DE users learned cue weights slower than the WADD users. This work advances the study of lexicographic choice strategies, both empirically and theoretically, and deepens our understanding of strategy selection, in particular the interaction between the strategy used and learning the structure of the environment. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Make-or-Break: Chasing Risky Goals or Settling for Safe Rewards?

Humans regularly pursue activities characterized by dramatic success or failure outcomes where, critically, the chances of success depend on the time invested working toward it. How should people allocate time between such make-or-break challenges and safe alternatives, where rewards are more predictable (e.g., linear) functions of performance? We present a formal framework for studying time allocation between these two types of activities, and we explore optimal behavior in both one-shot and dynamic versions of the problem. In the one-shot version, we illustrate striking discontinuities in the optimal time allocation policy as we gradually change the parameters of the decision-making problem. In the dynamic version, we formulate the optimal strategy-defined by a giving-up threshold-which adaptively dictates when people should stop pursuing the make-or-break goal. We then show that this strategy is computationally inaccessible for humans, and we explore boundedly rational alternatives. We compare the performance of the optimal model against (a) a myopic giving-up threshold that is easier to compute, and even simpler heuristic strategies that either (b) only decide whether or not to start pursuing the goal and never give up or (c) consider giving up at a fixed number of control points. Comparing strategies across environments, we investigate the cost and behavioral implications of sidestepping the computational burden of full rationality.

Publisher Correction: Social learning strategies for matters of taste.

The version of the Supplementary Information file that was originally published with this Article was not the latest version provided by the authors. In the captions of Supplementary Figs. 2 and 8, the median standard error values were reported to be 0.0028 in both cases; instead, in both instances, the values should have been 0.0015. These have now been updated and the Supplementary Information file replaced.

Social learning strategies for matters of taste.

Most choices people make are about 'matters of taste', on which there is no universal, objective truth. Nevertheless, people can learn from the experiences of individuals with similar tastes who have already evaluated the available options-a potential harnessed by recommender systems. We mapped recommender system algorithms to models of human judgement and decision-making about 'matters of fact' and recast the latter as social learning strategies for matters of taste. Using computer simulations on a large-scale, empirical dataset, we studied how people could leverage the experiences of others to make better decisions. Our simulations showed that experienced individuals can benefit from relying mostly on the opinions of seemingly similar people; by contrast, inexperienced individuals cannot reliably estimate similarity and are better off picking the mainstream option despite differences in taste. Crucially, the level of experience beyond which people should switch to similarity-heavy strategies varies substantially across individuals and depends on how mainstream (or alternative) an individual's tastes are and the level of dispersion in taste similarity with the other people in the group.

"Big data" needs an analysis of decision processes.

We demonstrate by means of a simulation that the conceptual map presented by Bentley et al. is incomplete without taking into account people's decision processes. Within the same environment, two decision processes can generate strikingly different collective behavior; in two environments that fundamentally differ in transparency, a single process gives rise to virtually identical behavior.

Social influence and the collective dynamics of opinion formation.

Social influence is the process by which individuals adapt their opinion, revise their beliefs, or change their behavior as a result of social interactions with other people. In our strongly interconnected society, social influence plays a prominent role in many self-organized phenomena such as herding in cultural markets, the spread of ideas and innovations, and the amplification of fears during epidemics. Yet, the mechanisms of opinion formation remain poorly understood, and existing physics-based models lack systematic empirical validation. Here, we report two controlled experiments showing how participants answering factual questions revise their initial judgments after being exposed to the opinion and confidence level of others. Based on the observation of 59 experimental subjects exposed to peer-opinion for 15 different items, we draw an influence map that describes the strength of peer influence during interactions. A simple process model derived from our observations demonstrates how opinions in a group of interacting people can converge or split over repeated interactions. In particular, we identify two major attractors of opinion: (i) the expert effect, induced by the presence of a highly confident individual in the group, and (ii) the majority effect, caused by the presence of a critical mass of laypeople sharing similar opinions. Additional simulations reveal the existence of a tipping point at which one attractor will dominate over the other, driving collective opinion in a given direction. These findings have implications for understanding the mechanisms of public opinion formation and managing conflicting situations in which self-confident and better informed minorities challenge the views of a large uninformed majority.

Peripheral neuropathies in Sjogren syndrome: a new reappraisal.

BACKGROUND: The prevalence of peripheral neuropathy in patients with Sjögren syndrome remains unclear owing to conflicting results in the published series, with numbers ranging from 2% to over 60% of Sjögren syndrome patients. Whether peripheral neuropathy is a feature of the systemic or glandular disease or whether it is related to a circulating antineuronal antibody remains also uncertain. METHODS: The authors reviewed the records of patients with primary Sjögren syndrome (pSS), fulfilling the Revised European-American Classification Criteria, seen in their department from 1992 to 2009. The patients with previously recorded neuropathic features were re-examined clinically and electrophysiologically. Other causes of polyneuropathy were excluded. The authors also searched for circulating antineural antibodies using immunofluorescence and western blot and for antibodies against muscarinic and nicotinic acetylcholine receptors as potential biomarkers. RESULTS: 509 cases met the diagnostic criteria for pSS. Among these, 44 patients were recorded as having neuropathic symptoms. After completing the evaluation, however, only nine (1.8%) had polyneuropathy with objective clinical signs and abnormal electrophysiological findings. The neuropathy was axonal in all, in five pure sensory and in four sensorimotor. The patients with peripheral neuropathy had extraglandular manifestations such as palpable purpura and vasculitis. No evidence of antineural autoimmunity was found, and no candidate biomarkers were identified. CONCLUSION: Polyneuropathy is a rare manifestation of pSS occurring in 1.8% of patients. In the majority of patients, it is a late event and frequently associated with systemic disease or risk factors for lymphoma development.

The use of an expert system of composite risk factors in breast cancer screening.

Mass Screening seems to be the only promising way to discover breast cancer patients at an early and more curable stage and a positive method improving the cost-effectiveness and compliance of mass screening is the use of prognostic factors, to identify the high-risk group, who alone then would be screened. In a 200 women sample, who had undergone screening for breast cancer with clinical examination and bilateral mammography, we calculated the Composite Risk Factors of six Characteristics (C6RF), which are family history for breast cancer, pregnancy history, menstrual history, history of cystic breast disease, history of regular breast clinical or self-examination and presence or not of breast lump, using an expert system in IBM-compatible personal computer. In these cases the average C6RF was 0.18 (SD +/- 0.19) in low-risk group and 2.61 (SD +/- 4.76) in high-risk group and all cases with C6RF values higher than 0.56 were put in the high-risk group. Under these conditions, the sensitivity of the C6RF method, in discovering breast cancer, was 90% and the specificity 81.5% and the C6RF method was proved to be clinically valuable in identifying the high-risk group and controlling breast cancer.